面向关系数据库知识发现的概率逻辑贝叶斯网络研究

项目名称： 面向关系数据库知识发现的概率逻辑贝叶斯网络研究

项目编号： No.61272209

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 王利民

作者单位： 吉林大学

项目金额： 80万元

中文摘要： 关系数据库知识发现是目前国内外研究的热点，它对于工业与医疗诊断、软件智能化、金融风险分析等需要关系数据挖掘的应用领域有着重要的实际意义。贝叶斯网络被视为关系模型和概率关系相结合的主要研究方向，但基于数值的观点无法进行条件推理。本项目以关系数据规范化理论为基础，结合条件事件代数建立数据-因果依赖描述框架，根据概率推理规则进行依赖关系约简并进而简化概率推理过程，通过对信息论的扩展性研究揭示数据依赖所蕴含的信息流动方式，从而构造可以完备描述依赖关系的概率逻辑贝叶斯网络空间数据模型。同时分别从数据依赖和互信息角度分析条件独立性和d-分隔之间的联系，基于乘积空间条件事件代数将依赖关系概念性描述映射成贝叶斯网络的局部扩展结构，解决概率逻辑贝叶斯网络的实现问题。本项目融合基于数值和代数的观点构建贝叶斯网络，既是为了满足社会各行业对关系数据库知识发现的迫切需求，也可为贝叶斯网络研究提供新思路和理论依据。

中文关键词： 知识发现；贝叶斯网络；关系数据库；概率逻辑推理；高斯函数

英文摘要： Knowledge discovery of relational database is one of the hottest research topics at home and abroad, it has important practical significance for industrial and medical diagnosis, intelligent software, financial risk analysis and other data mining application fields. Bayesian network is considered as the main research direction for combination of relation model and probabilistic relations, but it can't apply conditional reasoning from numerical point of view. Based on relational normalization theory, this project combines with Conditional Event Algebra to build data-causal description framework, reduces dependency relationship and simplifies the procedure of probability reasoning by using the probabilistic reasoning rules, reveals the information flow schema implicated in data dependency based on research of information theory, then builds Probability-logic Bayesian network to completely describe all dependency relationships. And this project tries to analyze the relationship between conditional independence and d-separation from the viewpoint of data dependency and mutual information, project the description of relational dependency to local structure of Bayesian network based on Production Space Conditional Event Algebra, thus solves the problem of realization of Probability-logic Bayesian network. This project

英文关键词： Knowledge Discovery；Bayesian Network；Relational Database；Probabilistic Logic Inference；Gaussian Function